| Autor: |
Anthony Yeung, Andrea Iaboni, Elizabeth Rochon, Monica Lavoie, Calvin Santiago, Maria Yancheva, Jekaterina Novikova, Mengdan Xu, Jessica Robin, Liam D. Kaufman, Fariya Mostafa |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Alzheimer’s Research & Therapy, Vol 13, Iss 1, Pp 1-10 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1758-9193 |
| DOI: |
10.1186/s13195-021-00848-x |
| Popis: |
Abstract Background Language impairment is an important marker of neurodegenerative disorders. Despite this, there is no universal system of terminology used to describe these impairments and large inter-rater variability can exist between clinicians assessing language. The use of natural language processing (NLP) and automated speech analysis (ASA) is emerging as a novel and potentially more objective method to assess language in individuals with mild cognitive impairment (MCI) and Alzheimer’s dementia (AD). No studies have analyzed how variables extracted through NLP and ASA might also be correlated to language impairments identified by a clinician. Methods Audio recordings (n=30) from participants with AD, MCI, and controls were rated by clinicians for word-finding difficulty, incoherence, perseveration, and errors in speech. Speech recordings were also transcribed, and linguistic and acoustic variables were extracted through NLP and ASA. Correlations between clinician-rated speech characteristics and the variables were compared using Spearman’s correlation. Exploratory factor analysis was applied to find common factors between variables for each speech characteristic. Results Clinician agreement was high in three of the four speech characteristics: word-finding difficulty (ICC = 0.92, p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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